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Overview

  • Introduction
    • Quickstart Example
    • Contributing
    • Contribution Guidelines
    • GitHub
  • New in coremltools
    • Release Notes
    • Migrating to coremltools 4 and Newer
  • Installation
  • FAQs

Unified Conversion API

  • Unified Conversion API
    • ML Programs
    • Comparing ML Programs and Neural Networks
    • Quantization
    • Typed Execution
    • Typed Execution Workflow Example
  • TensorFlow Conversion
    • TensorFlow 1 Conversion
    • Convert a TensorFlow 1 Image Classifier
    • Convert a TensorFlow 1 DeepSpeech Model
    • TensorFlow 2 Conversion
    • Convert TensorFlow 2 BERT Transformer Models
  • PyTorch Conversion
    • Model Tracing
    • Model Scripting
    • Convert a Natural Language Processing Model
    • Convert a torchvision Model from PyTorch
    • Convert a PyTorch Segmentation Model
  • Conversion Options
    • Image Inputs
    • Classifiers
    • Flexible Input Shapes
    • Composite Operators
    • Custom Operators
  • Model Intermediate Language

Other Converters

  • Trees and Linear Models
    • LibSVM
    • Scikit-learn
    • XGBoost
  • Legacy Neural Networks
    • Multi-backend Keras
    • ONNX

MLModel

  • MLModel Overview
  • Xcode Model Preview Types
  • MLModel Utilities
  • Model Prediction
  • Updatable Models Overview
    • Neural Network Classifier
    • Pipeline Classifier
    • Nearest Neighbor Classifier

Trees and Linear Models

Suggest Edits

You can convert the following trees and linear models to Core ML:

  • LibSVM
  • Scikit-learn
  • XGBoost

Updated over 4 years ago


What’s Next
  • LibSVM
  • Scikit-learn
  • XGBoost